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<h1>Chromatogram deconvolution</h1>

<h2>Wavelets (XCMS)</h2>

<p>
    This method uses wavelets to detect peaks within a chromatogram. A series of wavelets of different scales
    is convolved with the chromatogram. Local maxima in the convolution results determine the locations of possible
    peaks. When these candidate peak locations co-occur at multiple scales then the scale with the strongest response
    indicates peak width. Given the candidate peak locations and scales, peaks can then be reconstructed from the
    original chromatogram. Full details of the algorithm are published in Tautenhahn et al. [<a href="#ref1">1</a>].
</p>

<h4>Method parameters</h4>

<dl>
    <dt>S/N Threshold</dt>
    <dd>Peaks with a signal-to-noise ratio less than the threshold will be rejected. The S:N ratio is defined as
        (<span style="font-style: italic;">max</span>&nbsp;-&nbsp;<span style="font-style: italic;">baseline</span>)&nbsp;/&nbsp;<span
                style="font-style: italic;">sd</span>, where <span style="font-style: italic;">max</span> is the maximum
        peak intensity, <span style="font-style: italic;">baseline</span>
        is the estimated baseline value, and <span
                style="font-style: italic;">sd</span> is the standard deviation of local chromatographic noise.
    </dd>

    <dt>Peak scales</dt>
    <dd>The range of peak scales to search for. Scales are expressed as RT values (minutes) and correspond to the range
        of wavelet scales that will be applied to the chromatogram. If the minimum scale is too small then noise may be
        detected as peaks. If the maximum scale is to low then broad peaks may be ignored.
    </dd>

    <dt>Peak duration range</dt>
    <dd>The acceptable range of peak widths. Peaks with widths outside this range will be rejected.</dd>

    <dt>Integration method</dt>
    <dd>When reconstructing a peak from the chromatogram, gradient descent is used. This can be performed on the raw
        peak data or a smoothed version of it. The former is more accurate but can be susceptible to noise. The latter
        is less exact but more robust in the presence of noise.
    </dd>
</dl>

<h4>Requirements</h4>

<p>
    The Wavelets detector relies on Bioconductor's XCMS package for R [<a href="#ref2">2</a>]. Therefore, you must
    have R v2.15 or later installed. To install the XCMS package, run R and issue the following commands:
</p>

<pre>source("http://bioconductor.org/biocLite.R")
biocLite("xcms")</pre>

<p>To run R from MZmine the Rserve package [<a href="#ref3">3</a>] must be installed in R, so also run the following R
    command:</p>

<pre>install.packages("Rserve")</pre>

<h2>References</h2>

<p>
    <a name="ref1"></a>
    [1] Ralf Tautenhahn, Christoph Böttcher, and Steffen Neumann "Highly sensitive feature detection for high resolution
    LC/MS" <span style="font-style: italic;">BMC Bioinformatics</span> <span style="font-weight: bold;">2008</span>,
    9:504
</p>

<p>
    <a name="ref2"></a>
    [2] Bioconductor XCMS "LC/MS and GC/MS Data Analysis" <a
        href="http://www.bioconductor.org/packages/release/bioc/html/xcms.html">http://www.bioconductor.org/packages/release/bioc/html/xcms.html</a>.
</p>

<p>
    <a name="ref3"></a> [3] Rserve "A TCP/IP server which allows other programs to use facilities of R" <a href="https://rforge.net/Rserve/">https://rforge.net/Rserve/</a>.
</p>
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